Uncertainty in detecting the disturbance history of forest ecosystems using dendrochronology

QuestionsUncertainty in detecting disturbance histories has long been ignored in dendrochronological studies in forest ecosystems. Our goal was to characterize this uncertainty in relation to the key parameters of forest ecosystems and sample size. In addition, we aimed to provide a method to define uncertainty bounds in specific forest ecosystems with known parameters, and to provide a required (conservative) minimal sample size to achieve a pre-defined level of uncertainty if no actual key forest parameters are known.LocationTraining data were collected from Žofínský Prales (48°40′N, 14°42′E, 735–830 m a.s.l., granite, Czech Republic).MethodsWe used probability theory and expressed uncertainty as the length (the difference between the upper and lower bounds) of the 95% confidence interval. We studied the uncertainty of (i) the initial growth of trees – if they originated under canopy or in a gap; and (ii) the responses to disturbance events during subsequent growth – on the basis of release detection in the radial growth of trees. These two variables provide different information, which together give a picture of the disturbance history. While initial growth date the existence of a gap in a given decade (recent as well as older gaps are included), release demonstrates the moment of a disturbance event.ResultsWith the help of general mathematical deduction, we have obtained results valid across vegetation types. The length of a confidence interval depends on the sample size, proportion of released trees in a population, as well as on the variability of tree layer features (e.g., crown area of suppressed and released trees).ConclusionsMost studies to date have evaluated the initial growth of trees with higher uncertainty than for canopy disturbed area. The length of the 95% confidence interval for detecting initial growth has been rarely shorter than 0.1 (error ± 5%) and has mostly been much longer. To reach 95% confidence interval length of 0.1 (error ± 5%) when detecting the canopy disturbed area, at least 485 tree cores should be evaluated in studied time period, while to reach a 0.05 interval length (error ± 2.5%) at least 1925 tree cores are required. Our approach can be used to find the required sample size in each specific forest ecosystem to achieve pre-defined levels of uncertainty while detecting disturbance history.     

A comparative assessment of support vector regression,artificial neural networks,and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape

Soil organic carbon (SOC) is a key indicator of ecosystem health, with a great potential to affect climate change. This study aimed to develop, evaluate, and compare the performance of support vector regression (SVR), artificial neural network (ANN), and random forest (RF) models in predicting and mapping SOC stocks in the Eastern Mau Forest Reserve, Kenya. Auxiliary data, including soil sampling, climatic, topographic, and remotely-sensed data were used for model calibration. The calibrated models were applied to create prediction maps of SOC stocks that were validated using independent testing data. The results showed that the models overestimated SOC stocks. Random forest model with a mean error (ME) of −6.5 Mg C ha⁻¹ had the highest tendency for overestimation, while SVR model with an ME of −4.4 Mg C ha⁻¹ had the lowest tendency. Support vector regression model also had the lowest root mean squared error (RMSE) and the highest R² values (14.9 Mg C ha⁻¹ and 0.6, respectively); hence, it was the best method to predict SOC stocks. Artificial neural network predictions followed closely with RMSE, ME, and R² values of 15.5, −4.7, and 0.6, respectively. The three prediction maps broadly depicted similar spatial patterns of SOC stocks, with an increasing gradient of SOC stocks from east to west. The highest stocks were on the forest-dominated western and north-western parts, while the lowest stocks were on the cropland-dominated eastern part. The most important variable for explaining the observed spatial patterns of SOC stocks was total nitrogen concentration. Based on the close performance of SVR and ANN models, we proposed that both models should be calibrated, and then the best result applied for spatial prediction of target soil properties in other contexts.     

A power-driven increment borer for sampling high-density tropical wood

High-density hardwood trees with large diameters have been found to damage manually operated increment borers, thus limiting their use in the tropics. Therefore, we herein report a new, low-cost gasoline-powered sampling system for high-density tropical hardwood trees with large diameters. This system provides increment cores 15 mm in diameter and up to 1.35 m in length, allowing minimally invasive sampling of tropical hardwood tree species, which, up to the present, could not be collected by conventional 5 or 10 mm increment borers. This system provides a single core sample with ample amount of wood for multidisciplinary analyses, including ring width, stable isotope and wood anatomical measurements. The borer never gets stuck inside stems, even in hollowed trees, cores will never twist during coring, and the gasoline drill gives ample flexibility in the field. It is anticipated that the dendrochronological community will find our technique very useful in the pursuit of tropical tree ring research.     

The Impact of Fine-Scale Disturbances on the Predictability of Vegetation Dynamics and Carbon Flux

Predictions from forest ecosystem models are limited in part by large uncertainties in the current state of the land surface, as previous disturbances have important and lasting influences on ecosystem structure and fluxes that can be difficult to detect. Likewise, future disturbances also present a challenge to prediction as their dynamics are episodic and complex and occur across a range of spatial and temporal scales. While large extreme events such as tropical cyclones, fires, or pest outbreaks can produce dramatic consequences, small fine-scale disturbance events are typically much more common and may be as or even more important. This study focuses on the impacts of these smaller disturbance events on the predictability of vegetation dynamics and carbon flux. Using data on vegetation structure collected for the same domain at two different times, i.e. “repeat lidar data”, we test high-resolution model predictions of vegetation dynamics and carbon flux across a range of spatial scales at an important tropical forest site at La Selva Biological Station, Costa Rica. We found that predicted height change from a height-structured ecosystem model compared well to lidar measured height change at the domain scale (~150 ha), but that the model-data mismatch increased exponentially as the spatial scale of evaluation decreased below 20 ha. We demonstrate that such scale-dependent errors can be attributed to errors predicting the pattern of fine-scale forest disturbances. The results of this study illustrate the strong impact fine-scale forest disturbances have on forest dynamics, ultimately limiting the spatial resolution of accurate model predictions.     

Loss of aboveground forest biomass and landscape biomass variability in Missouri,US

Disturbance regimes and forests have changed over time in the eastern United States. We examined effects of historical disturbance (circa 1813 to 1850) compared to current disturbance (circa 2004 to 2008) on aboveground, live tree biomass (for trees with diameters ≥13 cm) and landscape variation of biomass in forests of the Ozarks and Plains landscapes in Missouri, USA. We simulated 10,000 one-hectare plots using random diameters generated from parameters of diameter distributions limited to diameters ≥13 cm and random densities generated from density estimates. Area-weighted mean biomass density (Mg/ha) for historical forests averaged 116 Mg/ha, ranging from 54 Mg/ha to 357 Mg/ha by small scale ecological subsections within Missouri landscapes. Area-weighted mean biomass density for current forests averaged 82 Mg/ha, ranging from 66 Mg/ha to 144 Mg/ha by ecological subsection for currently forested land. Biomass density of current forest was greater than historical biomass density for only 2 of 23 ecological subsections. Current carbon sequestration of 292 TgC on 7 million ha of forested land is less than half of the estimated historical total carbon sequestration of 693 TgC on 12 million ha. Cumulative tree cutting disturbances over time have produced forests that have less aboveground tree biomass and are uniform in biomass compared to estimates of historical biomass, which varied across Missouri landscapes. With continued relatively low rates of forest disturbance, current biomass per ha will likely increase to historical levels as the most competitive trees become larger in size and mean number of trees per ha decreases due to competition and self-thinning. Restoration of large diameter structure and forested extent of upland woodlands and floodplain forests could fulfill multiple conservation objectives, including carbon sequestration.     

Defining quantitative stream disturbance gradients and the additive role of habitat variation to explain macroinvertebrate taxa richness

Most studies dealing with the use of ecological indicators and other applied ecological research rely on some definition or concept of what constitutes least-, intermediate- and most-disturbed condition. Currently, most rigorous methodologies designed to define those conditions are suited to large spatial extents (nations, ecoregions) and many sites (hundreds to thousands). The objective of this study was to describe a methodology to quantitatively define a disturbance gradient for 40 sites in each of two small southeastern Brazil river basins. The assessment of anthropogenic disturbance experienced by each site was based solely on measurements strictly related to the intensity and extent of anthropogenic pressures. We calculated two indices: one concerned site-scale pressures and the other catchment-scale pressures. We combined those two indices into a single integrated disturbance index (IDI) because disturbances operating at both scales affect stream biota. The local- and catchment-scale disturbance indices were weakly correlated in the two basins (r = 0.21 and 0.35) and both significantly (p < 0.05) reduced site EPT (insect orders Ephemeroptera, Plecoptera, Trichoptera) richness. The IDI also performed well in explaining EPT richness in the basin that presented the stronger disturbance gradient (R² = 0.39, p < 0.001). Natural habitat variability was assessed as a second source of variation in EPT richness. Stream size and microhabitats were the key habitat characteristics not related to disturbances that enhanced the explanation of EPT richness over that attributed to the IDI. In both basins the IDI plus habitat metrics together explained around 50% of EPT richness variation. In the basin with the weaker disturbance gradient, natural habitat explained more variation in EPT richness than did the IDI, a result that has implications for biomonitoring studies. We conclude that quantitatively defined disturbance gradients offer a reliable and comprehensive characterization of anthropogenic pressure that integrates data from different spatial scales.     

Mapping multiple plant species abundance patterns - A multiobjective optimization procedure for combining reflectance spectroscopy and species ordination

Nature conservation and ecological restoration crucially depends on the knowledge about spatial patterns of plant species that control habitat conversion and disturbance regimes. Especially, species abundances are capable of indicating early development tendencies for setting habitat management strategies. This study demonstrates the transfer of field spectroscopy to hyperspectral imagery to map multiple plant species abundances in an open dryland area using two imaging spectrometers in two different phenological phases. We show that species abundances can partially be described by multiple gradients forming different coordinates in a contour map. For this purpose, species abundances were projected into an ordination space using non-metric multidimensional scaling and subsequent spatial interpolation. It was demonstrated that different gradients can be modeled in a Partial Least Squares regression framework resulting in distinct spectral features for certain gradient directions. We combine both objectives in a multiobjective NSGA-II procedure to maximize the quantitative determination of species abundance in ordination and spectral predictability in related field spectra, simultaneously. NSGA-II was finally used to select optimal spectral models for n = 35 single species that were transferred to hyperspectral imagery for mapping purpose. We can show that abundance predictabilities can be evaluated on the basis of individual model performances that hold different spectral features for each species in a designated phenological phase. Finally, we present spatially explicit multi-species maps for the best n = 18 and abundance maps for n = 8 models that could be linked to patterns of species richness, coexistence, succession stages and habitat type conditions.     

Extinction thresholds and negative responses of Afrotropical ant-following birds to forest cover loss in oil palm and agroforestry landscapes

Afrotropical ant-following birds are vulnerable to forest loss and disturbance, but critical habitat thresholds regarding their abundance and species richness in human-dominated landscapes, including industrial oil palm plantations, have never been assessed. We measured forest cover through Landsat imagery and recorded species richness and relative abundance of 20 ant-following birds in 48 plots of 1-km², covering three landscapes of Southwest Cameroon: Korup National Park, smallholder agroforestry areas (with farms embedded in forest), and an industrial oil palm plantation. We evaluated differences in encounter frequency and species richness among landscapes, and the presence of critical thresholds through enhanced adaptive regression through hinges. All species were detected in Korup National Park and the agroforestry landscape, which had similar forest cover (>85%). Only nine species were found in the oil palm plantation (forest cover = 10.3 ± 3.3%). At the 1-km² scale, the number of species and bird encounters were comparable in agroforests and the protected area: mean species richness ranged from 12.2 ± 0.6 in the park and 12.2 ± 0.6 in the agroforestry matrix to 1.0 ± 0.4 in the industrial oil palm plantation; whereas encounters decreased from 34.4 ± 3.2 to 26.1 ± 2.9 and 1.3 ± 0.4, respectively. Bird encounters decreased linearly with decreasing forest cover, down to an extinction threshold identified at 24% forest cover. Species richness declined linearly by ca. one species per 7.4% forest cover lost. We identified an extinction threshold at 52% forest cover for the most sensitive species (Criniger chloronotus, Dicrurus atripennis, and Neocossyphus poensis). Our results show that substantial proportions of forests are required to sustain complete ant-following bird assemblages in Afrotropical landscapes and confirm the high sensitivity of this bird guild to deforestation after industrial oil palm development. Securing both forest biodiversity and food production in an Afrotropical production landscape may be best attained through a combination of protected areas and wildlife-friendly agroforestry.     

Fusion of airborne LiDAR data and hyperspectral imagery for aboveground and belowground forest biomass estimation

Vegetation biomass is a key biophysical parameter for many ecological and environmental models. The accurate estimation of biomass is essential for improving the accuracy and applicability of these models. Light Detection and Ranging (LiDAR) data have been extensively used to estimate forest biomass. Recently, there has been an increasing interest in fusing LiDAR with other data sources for directly measuring or estimating vegetation characteristics. In this study, the potential of fused LiDAR and hyperspectral data for biomass estimation was tested in the middle Heihe River Basin, northwest China. A series of LiDAR and hyperspectral metrics were calculated to obtain the optimal biomass estimation model. To assess the prediction ability of the fused data, single and fused LiDAR and hyperspectral metrics were regressed against field-observed belowground biomass (BGB), aboveground biomass (AGB) and total forest biomass (TB). The partial least squares (PLS) regression method was used to reduce the multicollinearity problem associated with the input metrics. It was found that the estimation accuracy of forest biomass was affected by LiDAR plot size, and the optimal plot size in this study had a radius of 22 m. The results showed that LiDAR data alone could estimate biomass with a relative high accuracy, and hyperspectral data had lower prediction ability for forest biomass estimation than LiDAR data. The best estimation model was using a fusion of LiDAR and hyperspectral metrics (R² = 0.785, 0.893 and 0.882 for BGB, AGB and TB, respectively, with p < 0.0001). Compared with LiDAR metrics alone, the fused LiDAR and hyperspectral data improved R² by 5.8%, 2.2% and 2.6%, decreased AIC value by 1.9%, 1.1% and 1.2%, and reduced RMSE by 8.6%, 7.9% and 8.3% for BGB, AGB and TB, respectively. These results demonstrated that biomass accuracies could be improved by the use of fused LiDAR and hyperspectral data, although the improvement was slight when compared with LiDAR data alone. This slight improvement could be attributed to the complementary information contained in LiDAR and hyperspectral data. In conclusion, fusion of LiDAR and other remotely sensed data has great potential for improving biomass estimation accuracy.     

Shape complexity of space used by American black bears influenced by sex and intensity of use

Disentangling the complexities that influence animal space use poses substantial challenges based on decision trade-offs and constraints imposed on animals. Optimal decisions suggest that the spatial complexity of home-range shapes should be inversely related to energy conservation and fitness. Hence, the most beneficial shape should be the circle. We evaluated whether shape complexity (i.e., an index of circularity) of home ranges was influenced by two extrinsic (spatial heterogeneity, preferred habitat [i.e., deciduous forest]), and three intrinsic (sex, season [breeding, non-breeding], intensity of use) factors, with intensity of use indexed as contours containing core and peripheral areas. We estimated utilization distributions of 39 radiomarked adult American black bears (Ursus americanus), a habitat generalist, using fixed-kernel techniques and estimated 50% (core area) and 95% (peripheral area) contours. We fit a set of 47 models using linear modeling and ranked models using small-sample Akaike Information Criterion. Coefficients for the best model were the intrinsic factors intensity of use (reference category = core; 0.118; 95% CL = 0.064–0.173), sex (reference = female; 0.105; 95% CL = 0.043–0.167), and intercept (0.229; 95% CL = 0.186–0.272). Shape complexity was less for core areas than peripheral areas and less for females than males. Considering complex resource selection patterns within a fragmented landscape, both sexes appeared to use energy-maximizing strategies, although the increase in shape complexity for males may be an allometric relationship based on size dimorphism. Our approach supported the phenomenon of optimality as manifested through home-range shape complexity, but we suggest that assessment of this phenomenon for habitat specialists may yield different results, including the potential importance of intrinsic factors based on more restrictive limiting factors.     

Evaluating the potential for dendrochronological analysis of live oak (Quercus virginiana Mill.) from the urban and rural environment—An explorative study

Live oak (Quercus virginiana Mill.) is a long lived, broadleaf, evergreen species native to the lower Coastal Plain of the southern and southeastern United States and is frequently planted in the urban environment. However, live oak has received limited attention for its potential for dendrochronological analyses possibly because its growth-ring boundaries may be less defined due to the absence of a distinct dormancy period. The objective of this explorative study was to determine whether live oak rings in both urban and historic trees could be accurately identified and measured for dendrochronological applications. One hundred urban live oaks were assessed as well as four cross-sectional specimens from historic sites in the southeastern United States. Age of all cored live oak ranged from 7 to 29 (average = 16.8 rings), while cross-sections were taken from trees with an average age of 175 years. A chronology was created from cores that had the highest interseries correlations (n = 27), resulting in an average interseries correlation of 0.654. The analysis of live oak cross sections revealed that the rings on live oak are very faint and barely discernible, delimited mainly by a faint row of earlywood vessels with no or barely visible terminal parenchyma. Rings on these sections were erratic (i.e., fading out along the circumference) and non-concentric (i.e., a particular ring could be both wide and narrow at certain points along the circumference) so that any attempt to measure the rings on opposing radii would have produced meaningless results. However, based on the results of this study, we conclude that the dendrochronological analysis of live oak is possible with careful sample preparation and analysis and that results would be reliable, even for urban trees. Results of this study suggest that urban tree-ring series with higher interseries correlations were more vigorous than those with lower values, suggesting that urban environmental stresses related to individual sites may decrease the usefulness of crossdating techniques for evaluating annual rings on younger, urban trees.     

Symmetry of the face in old age reflects childhood social status

The association of socioeconomic status (SES) with a range of lifecourse outcomes is robust, but the causes of these associations are not well understood. Research on the developmental origins of health and disease has led to the hypothesis that early developmental disturbance might permanently affect the lifecourse, accounting for some of the burden of chronic diseases such as coronary heart disease. Here we assessed developmental disturbance using bodily and facial symmetry and examined its association with socioeconomic status (SES) in childhood, and attained status at midlife. Symmetry was measured at ages 83 (facial symmetry) and 87 (bodily symmetry) in a sample of 292 individuals from the Lothian Birth Cohort 1921 (LBC1921). Structural equation models indicated that poorer SES during early development was significantly associated with lower facial symmetry (standardized path coefficient ?.25, p = .03). By contrast, midlife SES was not significantly associated with symmetry. The relationship was stronger in men (?.44, p = .03) than in women (?.12, p = .37), and the effect sizes were significantly different in magnitude (p = .004). These findings suggest that SES in early life (but not later in life) is associated with developmental disturbances. Facial symmetry appears to provide an effective record of early perturbations, whereas bodily symmetry seems relatively imperturbable. As bodily and facial symmetries were sensitive to different influences, they should not be treated as interchangeable. However, markers of childhood disturbance remain many decades later, suggesting that early development may account in part for associations between SES and health through the lifecourse. Future research should clarify which elements of the environment cause these perturbations.     

Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem

Soil organic carbon (SOC) plays an important role in soil fertility and carbon sequestration, and a better understanding of the spatial patterns of SOC is essential for soil resource management. In this study, we used boosted regression tree (BRT) and random forest (RF) models to map the distribution of topsoil organic carbon content at the northeastern edge of the Tibetan Plateau in China. A set of 105 soil samples and 12 environmental variables (including topography, climate and vegetation) were analyzed. The performance of the models was evaluated using a 10-fold cross-validation procedure. Maps of the mean values and standard deviations of SOC were generated to illustrate model variability and uncertainty. The results indicate that the BRT and RF models exhibited very similar performance and yielded similar predicted distributions of SOC. The two models explained approximately 70% of the total SOC variability. The BRT and RF models robustly predicted the SOC at low observed SOC values, whereas they underestimated high observed SOC values. This underestimation may have been caused by biased distributions of soil samples in the SOC space. Vegetation-related variables were assigned the highest importance in both models, followed by climate and topography. Both models produced spatial distribution maps of SOC that were closely related to vegetation cover. The SOC content predicted by the BRT model was clearly higher than that of the RF model in areas with greater vegetation cover because the contributions of vegetation-related variables in the two models (65% and 43%, respectively) differed significantly. The predicted SOC content increased from the northwestern to the southeastern part of the study area, average values produced by the BRT and RF models were 27.3 g kg⁻¹ and 26.6 g kg⁻¹, respectively. We conclude that the BRT and RF methods should be calibrated and compared to obtain the best prediction of SOC spatial distribution in similar regions. In addition, vegetation variables, including those obtained from remote sensing imagery, should be taken as the main environmental indicators and explicitly included when generating SOC maps in Alpine environments.     

Changes and interactions between forest landscape connectivity and burnt area in Spain

The spatial structure, functionality and dynamics of forest landscapes in peninsular Spain and the Balearic Islands were compared over the last five decades. Two particular features were studied in the sample sites: forest connectivity for wildlife and areas burnt by wildfires. 191 Squares, each 4 km × 4 km, were selected from the SISPARES (the monitoring framework designed to evaluate the trends in the structure of Spanish rural landscapes) environmental strata. Aerial photographs from 1956, 1984, 1998 and 2008 were interpreted and 11 land cover categories mapped and checked in the field, using a minimum mapping area of one hectare. The Equivalent Connected Area Index was used to assess forest connectivity over the sampling period. Social and economical factors were assessed using indicators of farm intensiveness. The Spanish forest connectivity has improved in the last five decades although two different trends can be identified: the first 40 years are characterized by positive rates of growth whereas the 10 last years are characterized by their stability. Nevertheless the area of burnt land was higher along the first 25 studied years and decreased significantly over the last decade.Our results show the climate is the main driver in the evolution of forest connectivity and burnt area in the forest landscapes, playing a direct role on forest biomass production and wildfire ignition and propagation, as well as an indirect role by keeping vertical and horizontal forest continuity through the landscape spatial pattern. Social and economic factors are very important drivers as well: Rural population density and farm size average have been tested as good indicators of landscape artificiality, highly correlated to wildfire hazard and forest connectivity.Finally, we have pointed out the evolutionary path followed by SISPARES framework as a tool for monitoring rural landscapes. It emphasises on the requirement of a 30 years time window for building-up reliable dynamic multifunctional model.     

Soundscape analysis and acoustic monitoring document impacts of natural gas exploration on biodiversity in a tropical forest

Natural resource extraction is increasing rapidly in tropical forests, but we lag behind in understanding the impacts of these disturbances on biodiversity. In high diversity tropical habitats, acoustic monitoring is an efficient tool for sampling a large proportion of the fauna across varied spatial and temporal scales. We used passive acoustic monitoring in a pre-montane forest in Peru to investigate how soundscape composition and richness of acoustic frequencies varied with distance from a natural gas exploratory well and with operational phase (construction and drilling). We also evaluated how anuran and avian species richness and vocal activity varied with distance and between phases. Soundscape analyses showed that acoustic frequency similarity was greatest among sites closer to (≤250 m) and farther from (≥500 m) the platform. Soundscapes revealed more frequencies were used during construction and showed a weak trend of increasing frequency richness with increasing distance from the disturbance. Avian species richness and detections increased with distance from the platform, but anuran richness and detections declined with distance. Operational phase did not play a significant role in overall richness or activity patterns of either group. Among birds, insectivore detections increased with distance from the platform, and nectarivores were detected more frequently during the drilling phase. Results demonstrate that acoustic monitoring and soundscape analyses are useful tools for evaluating the impact of development activity on the vocalizing community, and should be implemented as a best practice in monitoring biodiversity and for guiding specific mitigation strategies.     

Don’t miss the forest for the trees! Evidence for vertical differences in the response of plant diversity to disturbance in a tropical rain forest

Ecological studies in tropical rain forests traditionally focus on trees above a threshold diameter at breast height (dbh), since ignoring plant species of the other structural compartments is believed to be an acceptable tradeoff between exhaustiveness and effectiveness. However, the consequences of missing species below a threshold dbh value have been largely neglected so far. We evaluated whether the response of species diversity of ≥10-cm dbh trees was similar to the response of other structural ensembles (namely treelets, saplings, and terricolous herbs) in a lowland tropical rain forest, to three disturbance regimes: natural gap dynamics (control), and selective logging with and without additional thinning. We studied forest vegetation composition and diversity in a 20-yr replicated field experiment comprising nine 1 ha permanent plots established in a semi-deciduous rain forest of the Congo Basin and equally distributed among the three treatments. Once corrected by stem density, species richness was similar between logged (20 years since logging) and untouched old-growth forest stands with respect of trees, but higher with respect of treelets. As disturbance intensity increased, species richness increased within sapling layers but decreased within herb layers, while species spatial turnover (beta diversity) increased in both cases. Regarding the parameters of the partitioned rarefaction curves and relative abundance distribution curves, no correlation was found between trees and any of the other structural compartments. Whilst tree and treelet species composition was similar among treatments, the understories still reflected past disturbance intensity, with a strong response of the sapling and herb layers. These results show that ecological studies based solely on tree layers (dbh ≥ 10 cm) are misleading because their response to disturbance cannot be used as a surrogate for the response of other structural ensembles. Long-lasting effects of anthropogenic disturbance on the sapling bank and the herb layer may durably influence the long-term forest dynamics. Since overstory but not understory plant communities have recovered from human disturbances 20 years after silvicultural operations, African tropical rain forest ecosystems may not be as resilient to selective logging as previously thought.     

Mapping soil organic matter in low-relief areas based on land surface diurnal temperature difference and a vegetation index

Accurate estimates of the spatial variability of soil organic matter (SOM) are necessary to properly evaluate soil fertility and soil carbon sequestration potential. In plains and gently undulating terrains, soil spatial variability is not closely related to relief, and thus digital soil mapping (DSM) methods based on soil–landscape relationships often fail in these areas. Therefore, different predictors are needed for DSM in the plains. Time-series remotely sensed data, including thermal imagery and vegetation indices provide possibilities for mapping SOM in such areas. Two low-relief agricultural areas (Peixian County, 28 km × 28 km and Jiangyan County, 38 km × 50 km) in northwest and middle Jiangsu Province, east China, were chosen as case study areas. Land surface diurnal temperature difference (DTD) extracted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST), and soil-adjusted vegetation index (SAVI) at the peak of growing season calculated from Landsat ETM+ image were used as predictors. Regression kriging (RK) with a mixed linear model fitted by residual maximum likelihood (REML) and residuals interpolated by simple kriging (SK) were used to model and map SOM spatial distribution; ordinary kriging (OK) was used as a baseline comparison. The root mean squared error, mean error and mean absolute error calculated from leave-one-out cross-validation were used to assess prediction accuracy. Results showed that the proposed covariates provided added value to the observations. SAVI aggregated to MODIS resolution was able to identify local highs and lows not apparent from the DTD imagery alone. Despite the apparent similarity of the two areas, the spatial structure of residuals from the linear mixed models were quite different; ranges on the order of 3 km in Jiangyan but 16 km in Peixian, and accuracy of best models differed by a factor of two (3.3 g/kg and 6.3 g/kg SOM, respectively). This suggests that time-series remotely sensed data can provide useful auxiliary variable for mapping SOM in low-relief agricultural areas, with three important cautions: (1) image dates must be carefully chosen; (2) vegetation indices should supplement diurnal temperature differences, (3) model structure must be calibrated for each area.     

Airborne hyperspectral data predict Ellenberg indicator values for nutrient and moisture availability in dry grazed grasslands within a local agricultural landscape

Species-based ecological indices, such as Ellenberg indicators, reflect plant habitat preferences and can be used to describe local environment conditions. One disadvantage of using vegetation data as a substitute for environmental data is the fact that extensive floristic sampling can usually only be carried out at a plot scale within limited geographical areas. Remotely sensed data have the potential to provide information on fine-scale vegetation properties over large areas. In the present study, we examine whether airborne hyperspectral remote sensing can be used to predict Ellenberg nutrient (N) and moisture (M) values in plots in dry grazed grasslands within a local agricultural landscape in southern Sweden. We compare the prediction accuracy of three categories of model: (I) models based on predefined vegetation indices (VIs), (II) models based on waveband-selected VIs, and (III) models based on the full set of hyperspectral wavebands. We also identify the optimal combination of wavebands for the prediction of Ellenberg values. The floristic composition of 104 (4 m × 4 m grassland) plots on the Baltic island of Öland was surveyed in the field, and the vascular plant species recorded in the plots were assigned Ellenberg indicator values for N and M. A community-weighted mean value was calculated for N (mN) and M (mM) within each plot. Hyperspectral data were extracted from an 8 m × 8 m pixel window centred on each plot. The relationship between field-observed and predicted mean Ellenberg values was significant for all three categories of prediction models. The performance of the category II and III models was comparable, and they gave lower prediction errors and higher R² values than the category I models for both mN and mM. Visible and near-infrared wavebands were important for the prediction of both mN and mM, and shortwave infrared wavebands were also important for the prediction of mM. We conclude that airborne hyperspectral remote sensing can detect spectral differences in vegetation between grassland plots characterised by different mean Ellenberg N and M values, and that remote sensing technology can potentially be used to survey fine-scale variation in environmental conditions within a local agricultural landscape.     

The influence of natural disturbances on developmental patterns in Acadian mixedwood forests from 1946 to 2008

We used dendrochronological analyses to identify periods of natural disturbance and resulting growth responses in 32 mixed species stands in the Acadian forest. Stands represented five different development patterns, based upon 1946 softwood (SW) content (70–80%, termed SW versus 30–60%, termed mixedwood (MW)) and change in SW content from 1946 to 2006: SW-stable, SW-declining, MW-fluctuating, MW-stable, and MW-declining. Standardized growth chronologies were developed from 1163 increment cores sampled from balsam fir (Abies balsamea), red spruce (Picea rubens), yellow birch (Betula alleghaniensis), and sugar maple (Acer saccharum). Growth chronologies clearly identified three spruce budworm (Choristoneura fumiferana) outbreaks from 1914 to 1921, 1954 to 1961, and 1975 to 1984, and birch dieback from 1938 to 1948. Stand developmental patterns were caused by species characteristics and multiple interacting disturbances, resulting in mortality, growth reductions, releases, and establishment of new cohorts. Balsam fir was present in all stands, but its tendency to establish and release from advanced regeneration following budworm-caused mortality resulted in cyclical proportions of fir in the canopy. Red spruce was less vulnerable to spruce budworm and longer lived, allowing them to persist and better withstand disturbance. Periodic growth index values less than 0.9 for fir and spruce were correlated with mortality of softwoods caused by defoliation, which resulted in release and growth index values >1.1 for sugar maple. Our results demonstrated substantial variation in mixedwood development patterns over a 60-year period within a small (45 km) area.